Achievable Data Research Articles

A Study of Transmission Point Selection for Multi-Connectivity in Multi-Band Wireless Networks

In 5th generation mobile communication networks, the frequency band of the millimeter band of 30–100 GHz is supported to provide a transmission speed of 20 Gbps or higher. Furthermore, a huge transmission capacity, i.e., up to 1 Tbps, is one of the main requirements for the 6th generation mobile communication networks. In order to meet this requirement, the terahertz band is considered a new service band. Hence, we consider multi-band network environments, serving the sub-6Hz band, mmWave band, and additionally sub-terahertz band. Furthermore, we introduce the transmission point selection criteria with multiple connections for efficient multi-connectivity operation in a multi-band network environment, serving and receiving multiple connections from those bands at the same time. We also propose a point selection algorithm based on the selection criteria, e.g., achievable data rate. The proposed point selection algorithm attains lower computational complexity with a 2-approximation of the optimal solution. Our simulation results, applying the channel environment and beamforming in practical environments defined by 3GPP, show that selecting and serving multiple transmission points regardless of frequency band performs better than services through single-connectivity operation.

Optimizing data transmission in 6G software defined networks using deep reinforcement learning for next generation of virtual environments

Data transmission of Virtual Reality (VR) plays an important role in delivering a powerful VR experience. This increasing demand on both high bandwidth and low latency. 6G emerging technologies like Software Defined Network (SDN) and resource slicing are acting as promising technologies for addressing the transmission requirements of VR users. Efficient resource management becomes dominant to ensure a satisfactory user experience. The integration of Deep Reinforcement Learning (DRL) allows for dynamic network resource balancing, minimizing communication latency and maximizing data transmission rates wirelessly. Employing slicing techniques further aids in managing distributed resources across the network for different services as enhanced Mobile Broadband (eMBB) and Ultra-Reliable and Low Latency Communications (URLLC). The proposed VR-based SDN system model for 6G cellular networks facilitates centralized administration of resources, enhancing communication between VR users. This innovative solution seeks to contribute to the effective and streamlined resource management essential for VR video transmission in 6G cellular networks. The utilization of Deep Reinforcement Learning (DRL) approaches, is presented as an alternative solution, showcasing significant performance and feature distinctions through comparative results. Our results show that implementing strategies based on DRL leads to a considerable improvement in the resource management process as well as in the achievable data rate and a reduction in the necessary latency in dynamic and large scale networks.

ZEROES: Robust Derivative-Based Demodulation Method for Optical Camera Communication

Most of Optical Camera Communication (OCC) systems benefit from the rolling shutter mechanism of Complementary Metal-Oxide Semiconductor (CMOS) cameras to record the brightness evolution of the Light-Emitting Diode (LED) through dark and bright strips within images. While this technique enhances the maximum achievable data rate, the main difficulty lies in the demodulation of the signal extracted from images, subject to blooming effect. Thus, two main approaches were proposed to deal with this issue, using adaptive thresholds whose value evolves according to amplitude changes or detecting signal variations with the first-order derivative. As the second method is more robust, a new demodulation method based on the detection of the zeros of the first-order derivative of the extracted signal was proposed in this paper. Obtained results clearly show an improvement in the extracted signal demodulation compared to other methods, achieving a raw Bit Error Rate (BER) of 10−3 around 50 cm in a Line-Of-Sight scenario, and increasing the maximum communication distance by 43.5%, reaching 330 cm in the case of a Non-Line-Of-Sight transmission.

Analysis of interference effect in VL‐NOMA network considering signal power parameters performance

AbstractThis study analyses the interference effect in a visible light‐non‐orthogonal multiple access (VL‐NOMA) network that considers the signal power parameters performance for near and far users. The light‐emitting diode (LED) as a carrier transmits signals, and we investigate the interference effect. The interference effect challenge is a result of unaligned signal power parameters, thereby producing noise or echo during the signal transmission. The signal power parameters are successfully aligned, and NOMA techniques are deployed, which improves the signal performance in terms of bit‐error rate (BER), achieved data rate, and signal‐to‐interference plus noise ratio (SINR). Furthermore, the deployed NOMA techniques, such as power allocations (PA) to assign the signals appropriately, then superposition coding (SC) encodes the entire signal, and successive interference cancellation (SIC) cancels the interference within the signals. The signal behavior of the aligned and the unaligned signal power parameters performance are used to investigate the interference effect. We observed that unaligned signal power parameters reduce the signal performance of achieved data rate, BER, and SINR. Further, the aligned signal power parameter with NOMA techniques improves the signal performance. Moreover, in the aligned signal power scenario of NOMA, the near user performed better than the far user.

Performance Evaluation of Non-Lambertian SLIPT for 6G Visible Light Communication Systems

Visible light communication (VLC) has emerged as one promising candidate technique to improve the throughput performance in future sixth-generation (6G) mobile communication networks. Due to the limited battery capacity of VLC systems, light energy harvesting has been proposed and incorporated for achieving the simultaneous lightwave information and power transfer (SLIPT) function and for improving the overall energy efficiency. Nevertheless, almost all reported works are limited to SLIPT scenarios adopting a basic and well-discussed Lambertian optical transmitter, which definitely cannot characterize the potential and essential scenarios employing distinctive non-Lambertian optical transmitters with various spatial beam characteristics. For addressing this issue, in this work, SLIPT based on a distinct non-Lambertian optical beam configuration is investigated, and for further enhancing the harvested energy and the achievable data rate, the relevant flexible optical beam configuration method is presented as well. The numerical results show that, for a typical receiver position, compared with about 1.14 mJ harvested energy and a 31.2 Mbps achievable data rate of the baseline Lambertian configuration, a harvested energy gain of up to 1.55 mJ and an achievable data rate gain of 21.1 Mbps can be achieved by the non-Lambertian SLIPT scheme explored here.

Non-mechanical beam steering for high-speed optical wireless communications via electrowetting on dielectric

Electrowetting on dielectric (EWOD) is used for non-mechanical optical beam steering (OBS) in optical communication systems. High-capacitance ion gel is used to construct an efficient electrowetting interface that facilitates dynamic OBS. This integration facilitates precise control of beam steering and data transmission efficiency in optical wireless communication systems. An EWOD-based liquid prism (LP) manipulates beam direction via electrowetting. The theoretical framework is supported by the Young and Young-Lippmann equations for precise optical path control. We present a theoretical and experimental demonstration of a two-dimensional beam steering system using an EWOD-based LP, with beam steering up to 14.82° and 14.39° along the X and Y axes, respectively. The system achieves data rates of 1.9 Gbps in free-space optics (FSO) and 1.7 Gbps in underwater wireless optical communication (UWOC) systems, with a measured bit error rate that adheres to the standard threshold of the forward error correction limit. Our results suggest that the EWOD-based LP system offers a compact, efficient, and versatile design for optical devices in both FSO and UWOC systems.

Wavelength Dependence of Modal Bandwidth of Multimode Fibers for High Data Rate Transmission and Its Implications

Vertical-cavity surface-emitting laser (VCSEL)-based transmission over multimode fiber (MMF) has achieved data rates of 100G per lane and is progressing towards 200G per lane. Recently, high-data-rate MMFs derived from OM3 and OM4 have been proposed. These fibers exhibit higher effective modal bandwidths at 910 nm, leading to a different wavelength dependence compared to conventional OM3 and OM4 MMFs. Understanding the wavelength dependence of these fibers is crucial to address their utilization in a broader range of applications. Through Monte Carlo simulations, we have obtained the low-end boundary of the effective modal bandwidths (EMBs) for these fibers, revealing capability improvements over the existing OM3 and OM4. The high-data-rate OM4 performs the same as or better than OM5 from 840 nm to 920 nm, while also showing a high bandwidth for the 850–870 nm wavelength window, favoring VCSELs with center wavelengths shifted toward 860 nm. We also obtained the link bandwidth, which includes both modal bandwidth and chromatic dispersion contributions, and the transmission reaches for various types of transceivers. We find that for both high-data-rate OM3 and high-data-rate OM4, the link bandwidth stays above the value at 850 nm until around 910 nm, delivering a similar transmission performance from 850 to 910 nm without declining towards longer wavelengths, unlike the standard OM3 and OM4. This characteristic favors a wider range of wavelength choices for VCSELs and enables optimal deployments for various applications.

Photonic-assisted W-band flexible integrated sensing and communication system for fiber-wireless network based on CE-LFM-OFDM.

We have experimentally demonstrated a constant envelope linear frequency modulated orthogonal frequency division multiplexing (CE-LFM-OFDM) signal by employing an orthogonal frequency division multiplexing (OFDM) signal to phase modulate the linear frequency modulation (LFM) carrier. The experimental verification was conducted in the photonic-based integrated sensing and communication (ISAC) system working at 94.5 GHz. In our system, a 10-km optical fiber and a 1-m free space transmission are incorporated, achieving seamless fiber-wireless networks. As a result, we achieved data rates ranging from 8 to 15.4 Gbit/s and range resolution ranging from 1.5 to 7.5 cm, respectively.

Architecting CubeSat constellations for messaging service, Part I

In today’s modern and globalized world, connectivity is a key factor for businesses, production facilities, sensor networks, and ordinary people. However, there are still populated areas which are not covered by ground-based telecommunications infrastructure. This is where telecommunication satellite constellations come in, as they can provide coverage to remote and uninhabited regions and fill existing connectivity gaps to ensure data transfer.LoRa is one of the technologies designed for data transmissions over long distances with low power consumption. Alongside with other technologies of the Low-Power Wide Area Network family, it is widely used for Internet of Things applications. LoRa chirp spread spectrum modulation is robust against the Doppler frequency shifts encountered in low earth orbits, and it has already been used in IoT satellite communications. Due to the low transmitted signal power, the achieved data rate is not high, making it a suitable technology for telecommunications payloads on CubeSat platforms for messaging services. As compared to existing traditional communication satellite systems, CubeSat constellations are low-cost and may offer an affordable connectivity service to developing regions.This study is divided in two parts. In Part I the demand model is built based on the population distribution not covered by cell towers. The LoRa link performance is analyzed, considering the impact of LoRa channel parameters variation, such as spreading factor and channel bandwidth, while satellite orbital height, transmission antenna beamwidth, and transmitter peak power have a direct impact on the payload mass. Among thousands of possible configurations, 73 feasible payload designs have been downselected. In Part II of the study, the satellite mass and the total system cost are estimated based on the payload parameters obtained. Messages transmission simulation via a constellation is conducted in order to identify optimal constellation architectures for messaging service, as well as the main drivers of the system economic profitability.The presented analysis results provide a deeper understanding of LoRa connectivity advantages and limitations together with the performance drivers, which will support the optimization of future LoRa-based satellite communication systems and other IoT satellite constellations.

Interference aware path planning for mobile robots under joint communication and sensing in mmWave networks

The beyond fifth-generation (B5G) and emerging sixth-generation (6G) wireless networks are considered as key enablers in supporting a diversified set of applications for industrial mobile robots (MRs). In this paper, we consider mobile robots that autonomously roam on an industrial floor and perform a variety of tasks at different locations, whilst utilizing high directivity beamformers in millimeter wave (mmWave) small cells for joint communication and sensing. In such scenarios, the potential close proximity of MRs connected to different base stations, may cause excessive levels of interference having as a net result a decrease in the overall achievable data rate and network service degradation. To mitigate this effect an interference aware path planning algorithm is proposed by explicitly taking into account both achievable communication and sensing performance. More specifically, the proposed heuristic scheme aims to find paths with minimal interfering time whilst improving the overall joint communication and sensing quality of service. A wide set of numerical investigations reveal that the proposed heuristic path planning scheme for the mmWave networked mobile robots can improve the overall achievable communication throughput and the sensing mutual information by up to 35.6% and 23.6% respectively compared with an interference oblivious scheme. Most importantly, those gains are attained without penalizing noticeably the total travel time of the MRs.

A deep learning based approach for image retrieval extraction in mobile edge computing

AbstractDeep learning has been widely explored in 5G applications, including computer vision, the Internet of Things (IoT), and intermedia classification. However, applying the deep learning approach in limited-resource mobile devices is one of the most challenging issues. At the same time, users’ experience in terms of Quality of Service (QoS) (e.g., service latency, outcome accuracy, and achievable data rate) performs poorly while interacting with machine learning applications. Mobile edge computing (MEC) has been introduced as a cooperative approach to bring computation resources in proximity to end-user devices to overcome these limitations. This article aims to design a novel image reiterative extraction algorithm based on convolution neural network (CNN) learning and computational task offloading to support machine learning-based mobile applications in resource-limited and uncertain environments. Accordingly, we leverage the framework of image retrieval extraction and introduce three approaches. First, privacy preservation is strict and aims to protect personal data. Second, network traffic reduction. Third, minimizing feature matching time. Our simulation results associated with real-time experiments on a small-scale MEC server have shown the effectiveness of the proposed deep learning-based approach over existing schemes. The source code is available here: https://github.com/jamalalasadi/CNN_Image_retrieval.

IRS-aided NOMA-based communication architecture for 6G wireless networks: An enhanced error-control and reliable data transmission

Intelligent reflecting surface (IRS) is a key technology for sixth-generation (6G) wireless communication to obtain high energy and spectral efficiency with a minimum cost. On the other hand, non orthogonal multiple access (NOMA) enhances the spectrum efficiency of the network. The combining schemes are implemented to obtain error-free data at the receiver. This paper proposes an IRS-aided NOMA-based packet combining (PC) and aggressive packet combining (APC) schemes-assisted communication architecture to enhance service quality and achieve sustainable and resilient connectivity among users for 6G wireless networks. In this paper, the mathematical model has been derived for the instantaneous achievable data rate, bit error rate, outage probability, and packet correction capability. The derived analytical expressions are validated through detailed Monte Carlo simulations. The obtained results show that the proposed schemes outperform the conventional NOMA with PC and NOMA with APC schemes and facilitate enhanced error control and reliable data transmission capabilities. Moreover, the proposed schemes have been compared with the IRS-aided orthogonal multiple access (OMA) schemes, where IRS-aided OMA schemes are considered benchmarks of the proposed schemes. It is presented that the proposed schemes are superior to the conventional OMA with PC and APC schemes and IRS-aided OMA with PC and APC schemes.

Bandit approach for unmanned aerial vehicle-centric low earth orbit satellite selection

In this paper, the problem of optimal low earth orbit satellite (LEO-Sat) selection by unmanned aerial vehicle (UAV) in space air ground integrated networks (SAGINs) will be investigated. The primary objective is to maximize the UAV's achievable data rate while ensuring a prolonged remaining visible time for the selected LEO-Sat. This problem poses significant challenges due to the highly dynamic nature caused by the substantial relative velocity between UAV and LEO-Sats, making LEO-Sats' features, e.g., elevation angle, remaining visible time, and the available bandwidth strongly time-dependent. Moreover, some LEO-Sats will appear/disappear from UAV visibility within short time intervals. An online learning approach will be proposed to efficiently address this highly dynamic problem, where it will be modeled as contextual multi-armed bandit (MAB) with dynamic arms. Through this modeling, the time varying LEO-Sats' features as well as their dynamic appearance/disappearance will be taken into account while selecting the best LEO-Sat. Through extensive numerical analysis, the performance of the proposed approach nearly matches that of the highly complex optimal performance based on exhaustive searching with negligible time complexity, and it outperforms conventional schemes that individually maximize one of the LEO-Sats' features.

Internet of Medical Things (IoMT) optimization for healthcare: A deep learning-based interference avoidance model

The Internet of Medical Things (IoMT) is a significant component of the broader Internet of Things (IoT), IoMT is responsible for monitoring, collecting, and transmitting critical medical data to central medical computer centers. IoMT faces significant challenges, including energy efficiency, interference from other devices in the spectrum, latency, and security and privacy concerns. As a result, in hospital settings, the development of a precise and dependable IoMT system is crucial. Consequently, this investigation offered an innovative, accurate, and dependable IoMT method. The suggested model intends to reduce or eliminate interference caused by other transmitting devices sharing the same IoMT spectrum within hospitals or medical institutions. It also presents an interference-avoidance distributed deep learning model for IoMT to medical receptionists. This model uses data from the Lagrange optimization technique to determine the ideal distance between interfering devices and medical receptions. As a result, the required system signal-to-interference-plus-noise (SINRth) is met while maintaining the highest energy efficiency (EE) and system achievable data rate (R). The proposed analytical model and deep learning model demonstrate the efficacy of the proposed approach by achieving the required system signal-to-interference-plus-noise (SINRth) with the best energy efficiency (EE) and system achievable data rate (R) while suppressing interference to any medical receptions.

Enhancing data rate and energy efficiency of NOMA systems using reconfigurable intelligent surfaces for millimeter-wave communications

In this article, we employ reconfigurable intelligent surface (RIS) to aid a non-orthogonal multiple access (NOMA) system utilizing millimeter-wave (mmWave) communications. Different to recent works on the RIS-supported NOMA systems where the certain carrier frequency was often normalized (fc=1 Hz), we analyze specific impacts of high carrier frequencies in the mmWave band (fc≥30 GHz) on the performance of the RIS-supported NOMA system. In particular, we derive the exact expressions of achievable data rate (ADR) and energy efficiency (EE) of the RIS-supported NOMA system using mmWave band (RIS-mmWave-NOMA system). We compare the ADR and EE of the RIS-mmWave-NOMA system with those of mmWave-NOMA system without RIS, where there are only source-user channels. Numerical illustrations clarify the benefits of utilizing RIS as well as the great impacts of high fc in mmWave band on the ADR and EE of the RIS-mmWave-NOMA system. More specifically, when fc increases from 50 to 100 GHz, the transmit power of source should be increased 8 dBm to achieve the same ADR. Meanwhile, the maximal EE with fc=100 GHz is greatly lower than the maximal EE with fc=50 GHz even though higher transmit power is used. In other words, when fc increases, higher transmit power can be used for the ADR aims but this method cannot be used for EE purposes. In these circumstances, utilizing a larger number of reflecting elements (REs) in the RIS is a good method. By using N≥200 REs, the ADR and EE of the RIS-mmWave-NOMA system are significantly higher than those of mmWave-NOMA system without RIS. Finally, we confirm the exactness of the derived formulas through Monte-Carlo simulations.

Parallel wavelength-division-multiplexed signal transmission and dispersion compensation enabled by soliton microcombs and microrings

The proliferation of computation-intensive technologies has led to a significant rise in the number of datacenters, posing challenges for high-speed and power-efficient datacenter interconnects (DCIs). Although inter-DCIs based on intensity modulation and direct detection (IM-DD) along with wavelength-division multiplexing technologies exhibit power-efficient and large-capacity properties, the requirement of multiple laser sources leads to high costs and limited scalability, and the chromatic dispersion (CD) restricts the transmission length of optical signals. Here we propose a scalable on-chip parallel IM-DD data transmission system enabled by a single-soliton Kerr microcomb and a reconfigurable microring resonator-based CD compensator. We experimentally demonstrate an aggregate line rate of 1.68 Tbit/s over a 20-km-long SMF. The extrapolated energy consumption for CD compensation of 40-km-SMFs is ~0.3 pJ/bit, which is calculated as being around 6 times less than that of the commercial 400G-ZR coherent transceivers. Our approach holds significant promise for achieving data rates exceeding 10 terabits.

Link budget analysis of bi-directional LEO and GEO optical feeder links advancing the beam wander model’s accuracy

The telecommunications of the future rely on the concept of a three-dimensional architecture able to integrate terrestrial and non-terrestrial networks with the goal to ensure a reliable and high-speed connectivity to users located anywhere. In this context, free space optical communications constitute a candidate technology for feeder links, thanks to their advantages in terms of bandwidth and achievable data rates. Nonetheless, due to the propagation impediments encountered by an optical beam travelling through atmosphere, flexible and accurate instruments able to support the design of optical feeder links are needed. Therefore, in this paper a link budget numerical tool able to meet these requirements is presented and the link budget analysis for real optical feeder links is performed demonstrating its prediction accuracy by means of the comparison with experimental results for both low Earth orbit and geostationary Earth orbit based configurations. Finally, the limits of the conventional beam wander model are analyzed and overcome.

Adaptive resource allocation in NOMA-enabled backscatter communications systems

The integration of NOMA with Backscatter communication (BackCom) is a promising solution for developing a green future wireless network. However, system performance degrades with the deployment of multiple backscatter devices (BDs) in a network. Hence, energy efficiency (EE) maximization with proper resource allocation is among the primary concerns. In this regard, this paper proposes an adaptive resource allocation method for maximizing EE by simultaneously optimizing the transmission power from the base station (BS), power allocation coefficients, and reflection coefficients under the constraints of maximum allowable transmission power and minimum achievable data rate. Specifically, an iterative method based on a parametric transformation approach is adopted for maximizing EE by jointly optimizing the coefficients, in which the power allocation problem to the BDs is solved by an adaptive method that is based on improved proportionate normalized least mean square (IPNLMS) algorithm. Then, the system performance is evaluated, and the impact of different parameters is also studied it is observed that EE is significantly improved as compared to the existing scheme, and maximum at <span>η</span>=-0.5.

Advancing high-performance visible light communication with long-wavelength InGaN-based micro-LEDs

This study showcases a method for achieving high-performance yellow and red micro-LEDs through precise control of indium content within quantum wells. By employing a hybrid quantum well structure with our six core technologies, we can accomplish outstanding external quantum efficiency (EQE) and robust stripe bandwidth. The resulting 30 μm × 8 micro-LED arrays exhibit maximum EQE values of 11.56% and 5.47% for yellow and red variants, respectively. Notably, the yellow micro-LED arrays achieve data rates exceeding 1 Gbit/s for non-return-to-zero on–off keying (NRZ-OOK) format and 1.5 Gbit/s for orthogonal frequency-division multiplexing (OFDM) format. These findings underscore the significant potential of long-wavelength InGaN-based micro-LEDs, positioning them as highly promising candidates for both full-color microdisplays and visible light communication applications.

Federated learning-based trajectory prediction for dynamic resource allocation in moving small cell networks

With the evolution of fifth generation (5G) of mobile communication, vehicular edge computing (VEC) and moving small cell (MSC) network are gaining attention because of their capability to provide improved quality-of-service (QoS) to vehicular users. The ultimate goal of VEC-enabled MSC network is to diminish the vehicular penetration effect and path loss resulting in improved network performance for vehicular users. In this paper, we explore distributed resource block (RB) allocation for fronthaul links of MSCs with probabilistic mobility in VEC-enabled MSC network. The proposed work exploits the computational power of road side units (RSUs) deployed with VEC servers present along the road sides to allocate the resources to MSCs in a distributed manner by exchanging limited information, with the objective of maximizing the data rate achieved by MSC network. Moreover, we propose federated learning (FL)-based position prediction of MSCs to predict the trajectory of MSCs in advance for efficient prediction of resource allocation in MSC network. Simulations results are presented to compare the position prediction dependent distributed and centralized time interval dependent interference graph-based resource allocation to MSCs in terms of RB utilization and average achievable data rate of MSC network. For comparison, we further investigated centralized as well as distributed threshold time dependent interference graph-based allocation of resources to MSC network.